1. Field of the Invention
The present invention relates generally to systems and methods for performing discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) operations. The invention also relates to digital video compression and decompression, and more particularly to a video encoder and decoder for performing the discrete cosine transform and/or inverse discrete cosine transform with improved efficiency and reduced computational requirements.
2. Description of the Related Art
DSP theory provides a host of tools for the analysis and representation of signal data. The discrete cosine transform and its inverse are among the more ubiquitous of these tools in multimedia applications. The discrete cosine transform (DCT) of a discrete function f(j), j=0, 1, . . . N-1 is defined as             F      ⁡              (        k        )              =                            2          ⁢                      c            ⁡                          (              k              )                                      N            ⁢                        ∑                      j            =            0                                N            -            1                          ⁢                  xe2x80x83                ⁢                              f            ⁡                          (              j              )                                ·                      cos            ⁡                          [                                                                    (                                                                  2                        ⁢                        j                                            +                      1                                        )                                    ⁢                  k                  ⁢                                      xe2x80x83                                    ⁢                  π                                                  2                  ⁢                  N                                            ]                                            ,
where k=0,1, . . . , Nxe2x88x921, and       c    ⁡          (      k      )        =            {                                                  1                              2                                                                                        for                ⁢                                  xe2x80x83                                ⁢                k                            =              0                                                            1                                                              for                ⁢                                  xe2x80x83                                ⁢                k                            ≠              0                                          }        .  
The inverse discrete cosine transform (IDCT) is defined by             f      ⁡              (        j        )              =                  ∑                  k          =          0                          N          -          1                    ⁢              xe2x80x83            ⁢                        c          ⁡                      (            k            )                          ⁢                  F          ⁡                      (            k            )                          ⁢                  cos          ⁡                      [                                                            (                                                            2                      ⁢                      j                                        +                    1                                    )                                ⁢                k                ⁢                                  xe2x80x83                                ⁢                π                                            2                ⁢                N                                      ]                                ,
where j=0, 1, . . . Nxe2x88x921.
The discrete cosine transform may be used in a wide variety of applications and allows an arbitrary input array size. However, the straightforward DCT algorithm is often prohibitively time-consuming especially when executed on general purpose processors. In 1977, Chen et al. disclosed an efficient algorithm for performing the DCT in an article entitled xe2x80x9cA Fast Computational Algorithm for the Discrete Cosine Transformxe2x80x9d, published in IEEE Transactions on Communications, Vol. COM-25, No. 9, September, 1977, authored by Wen-Hsiung Chen, C. Harrison Smith and S. C. Fralick, which is hereby incorporated by reference. Fast DCT algorithms such as that disclosed by Chen et al. are significantly more efficient that the straightforward DCT algorithm. Nevertheless, there remains room for improvement, particularly when the algorithm is employed in specific circumstances.
Traditional xc3x9786 processors are not well adapted for the types of calculations used in signal processing. Thus, signal processing software applications on traditional xc3x9786 processors have lagged behind what was realizable on other processor architectures. There have been various attempts to improve the signal processing performance of xc3x9786-based systems. For example, microcontrollers optimized for digital signal processing computations (DSPs) have been provided on plug-in cards or the motherboard. These microcontrollers operated essentially as hardwired coprocessors enabling the system to perform signal processing functions.
As multimedia applications become more sophisticated, the demands placed on computers are redoubled. Microprocessors are now routinely provided with enhanced support for these applications. For example, many processors now support single-instruction multiple-data (SIMD) commands such as MX instructions. Advanced Micro Devices, Inc. (hereinafter referred to as AMD) has proposed and implemented 3DNow!(trademark), a set of floating point SIMD instructions on xc3x9786 processors starting with the AMD-K6(copyright)-2. The AMD-K6(copyright)-2 is highly optimized to execute the 3DNow!(trademark) instructions with minimum latency. Software applications written for execution on the AMD-K6(copyright)-2 may use these instructions to accomplish signal processing functions and the traditional xc3x9786 instructions to accomplish other desired functions.
The 3DNow! instructions, being SIMD commands, are xe2x80x9cvectoredxe2x80x9d instructions in which a single operation is performed on multiple data operands. Such instructions are very efficient for graphics and audio applications where simple operations are repeated on each sample in a stream of data. SIMD commands invoke parallel execution in superscalar microprocessors where pipelining and/or multiple execution units are provided.
Vectored instructions typically have operands that are partitioned into separate sections, each of which is independently operated upon. For example, a vectored multiply instruction may operate upon a pair of 32-bit operands, each of which is partitioned into two 16-bit sections or four 8-bit sections. Upon execution of a vectored multiply instruction, corresponding sections of each operand are independently multiplied. FIG. 1 illustrates the differences between a scalar (i.e., non-vectored) multiplication and a vector multiplication. To quickly execute vectored multiply instructions, microprocessors such as the AMD-K6(copyright)-2 use a number of multipliers in parallel.
FIG. 2 illustrates one embodiment of a representative computer system 100 such as the AMD-K6(copyright)-2 which is configured to support the execution of general-purpose instructions and parallel floating-point instructions. Computer system 100 may comprise a microprocessor 110, memory 112, bus bridge 114, peripheral bus 116, and a plurality of peripheral devices P1-PN. Bus bridge 114 couples to microprocessor 10, memory 112 and peripheral bus 116. Bus bridge 114 mediates the exchange of data between microprocessor 110, memory 112 and peripheral devices P1-PN.
Microprocessor 110 is a superscalar microprocessor configured to execute instructions if a variable length instruction set. A subset of the variable length instruction set is the set of SIMD (single-instruction multiple-data) floating-point instructions. Microprocessor 110 is optimized to execute the SIMD floating-point instructions in a single clock cycle. In addition, the variable length instruction set includes a set of xc3x9786 instructions (e.g. the instructions defined by the 80486 processor architecture).
Memory 112 stores program instructions which control the operation of microprocessor 110. Memory 112 additionally stores input data to be operated on by microprocessor 110, and output data generated by microprocessor 110, in response to the program instructions. Peripheral devices P1-PN are representative of devices such as network interface cards (e.g. Ethernet cards), modems, sound cards, video acquisition boards, data acquisition cards, external storage media, etc. Computer system 100 may be a personal computer, a laptop computer, a portable computer, a television, a radio receiver and/or transmitter, etc.
FIG. 3 illustrates one embodiment for microprocessor 110. Microprocessor 110 may be configured with 3DNow!(trademark) and MMX(copyright) technologies. Microprocessor 110 may comprise bus interface unit 224, predecode unit 212, instruction cache 214, decode unit 220, execution engine 230, and data cache 226. Microprocessor 110 may also include store queue 238 and an L2 cache 240. Additionally, microprocessor 110 may include a branch prediction unit and a branch resolution unit (not shown) to allow efficient speculative execution.
Predecode unit 212 may be coupled to instruction cache 214, which stores instructions received from memory 112 via bus interface unit 224 and predecode unit 212. Instruction cache 214 may also contain a predecode cache (not shown) for storing predecode information. Decode unit 220 may receive instructions and predecode information from instruction cache 214 and decode the instructions into component pieces. The component pieces may be forwarded to execution engine 230. The component pieces may be RISC operands. (Microprocessor 110 may be RISC-based superscalar microprocessor). RISC ops are fixed-format internal instructions, most of which are executable by microprocessor 10 in a single clock cycle. RISC operations may be combined to form every function of the xc3x9786 instruction set.
Execution engine 230 may execute the decoded instructions in response to the component pieces received from decode unit 220. As shown in FIG. 4, execution engine 230 may include a scheduler buffer 232 coupled to receive input from decode unit 220. Scheduler buffer 232 may be configured to convey decoded instructions to a plurality of execution pipelines 236A-236E in accordance with input received from instruction control unit 234. Execution pipelines 236A-236E are representative, and in other embodiments, varying numbers and kinds of pipelines may be included.
Instruction control unit 234 contains the logic necessary to manage out of order execution of instructions stored in scheduler buffer 232. Instruction control unit 34 also manages data forwarding, register renaming, simultaneous issue and retirement of RISC operations, and speculative execution. In one embodiment, scheduler buffer 232 holds up to 24 RISC operations at one time. When possible, instruction control unit 234 may simultaneously issue (from buffer 232) a RISC operation to each available execution unit 236.
Execution pipelines 236A-236E may include load unit 236A, store unit 236B, register X pipeline 236C, register Y pipeline 236D, and floating point unit 236E. Load unit 236A may receive input from data cache 226, while store unit 236B may interface to data cache 226 via a store queue 238. Store unit 236B and load unit 236A may be two-staged pipeline designs. Store unit 236B may perform memory writes. For a memory write operation, the store unit 236B may generate a physical address and the associated data bytes which are to be written to memory. These results (i.e. physical address and data bytes) may be entered into the store queue 238. Memory read data may be supplied by data cache 226 or by an entry in store queue 238 (in the case of a recent store). If the data is supplied by store queue 238, additional execution latency may be avoided.
Register X pipeline 236C and register Y pipeline 236D may each include a combination of integer, integer SIMD (e.g. MMX(copyright)), and floating-point SIMD (e.g. 3DNow!(trademark)) execution resources. Some of these resources may be shared between the two register pipelines. As suggested by FIG. 3, load unit 236A, store unit 236B, and register pipelines 236C-236D may be coupled to a register file 244 from which these units are configured to read source operands. In addition, load unit 236A and register pipelines 236C-236D may be configured to store destination result values to register file 244. Register file 244 may include physical storage for a set of architected registers.
Floating point unit 236E may also include a register file 242. Register file 242 may include physical storage locations assigned to a set of architected floating point registers. Floating point instructions (e.g. xc3x9787 floating point instructions, or IEEE 754/854 compliant floating point instructions) may be executed by floating point unit 236E, which reads source operands from register file 242 and updates destinations within register file 242 as well. Some or all of the registers of register file 244 may be logically mapped (i.e. aliased) onto the floating point registers of register file 242.
Execution pipeline 236E may contain a floating point unit designed to accelerate the performance of software which utilizes the xc3x9786 (or xc3x9787) floating point instructions. Execution pipeline 236E may include an adder unit, a multiplier unit, and a divide/square root unit, etc. Execution pipeline 236E may operate in a coprocessor-like fashion, in which decode unit 220 directly dispatches the floating point instructions to execute pipeline 236E. The floating point instructions may still be allocated in scheduler buffer 232 to allow for in-order retirement of instructions. Execution pipeline 236E and scheduler buffer 232 may communicate to determine when a floating point instruction is ready for retirement.
FIG. 5 illustrates one embodiment of the execution resources which may be associated with register X pipeline 236C and the register Y pipeline 236D. As shown in FIG. 5, scheduler buffer 232 may be coupled via Register X issue bus 301 to:
(1) scalar integer X ALU (arithmetic logic unit) 310A,
(2) SIMD integer ALU 310B,
(3) SIMD integer/floating-point multiplier 310C,
(4) SIMD integer shifter 310D, and
(5) SIMD floating-point ALU 310E.
In addition, scheduler buffer 232 may be coupled via Register Y issue bus 302 to:
(3) SIMD integer/floating-point multiplier 310C,
(4) SIMD integer shifter 310D,
(5) SIMD floating-point ALU 310E,
(6) SIMD integer ALU 310F, and
(7) scalar integer Y ALU 310G
Scalar integer X ALU 310A and SIMD integer ALU 310B may dedicated to Register X pipeline 236C. Similarly, scalar integer Y ALU 310G and SIMD integer ALU 310F may be dedicated to Register Y pipeline 236D. Therefore, both register pipelines may allow superscalar execution of scalar integer instructions and SIMD integer instructions. SIMD integer/floating-point multiplier 310C, SIMD integer shifter 310D and SIMD floating-point ALU 310E may be shared by Register X pipeline 236C and Register Y pipeline 236D.
Scalar Integer X ALU 310A may be configured to perform integer ALU operations, integer multiplications, integer divisions (both signed and unsigned), shifts, and rotations. Scalar Integer Y ALU 310G may be configured to perform basic word and double word ALU operations (e.g. add, or, and, cmp, etc.).
SIMD integer ALU 310B and SID integer ALU 310F may be configured to perform addition, subtraction, logical, pack, and unpack operations on packed integer operands. In one embodiment, ALUs 310B and 310F are configured to perform addition, subtraction, logical, pack and unpack operations corresponding to the MMX (copyright) instruction set architecture.
SIMD integer/floating-point multiplier 310C may be configured to perform multiply operations on packed floating-point operands or packed integer operands. In one embodiment, multiplier 310C may be configured to perform integer multiply operations corresponding to the MMX(copyright) instruction set, and floating-point multiply operations corresponding to the 3DNow!(trademark) instruction set.
SIMD floating-point ALU 310E may be configured to perform packed floating-point addition, subtraction, comparison, and integer conversion operations on packed floating-point operands. In one embodiment, ALU 310E may be configured to perform packed floating-point addition, subtraction, comparison, and integer conversion operations corresponding to the 3DNow!(trademark) instruction set.
Any pair of operations which do not require a common resource (execution unit) may be simultaneously executed in the two register pipelines (i.e. one operation per pipeline). For example, a packed floating-point multiply and a packed floating-point addition may be issued and executed simultaneously to units 310C and 310E respectively. However, a packed integer multiply and a packed floating-point multiply could not be issued simultaneously in the embodiment of FIG. 5 without inducing a resource contention (for SIMD integer/floating-point multiplier 310C) and a stall condition. Thus, the maximum rate of execution for the two pipelines taken together is equal to two operations per cycle.
Register file 244 may contain registers which are configured to support packed integer and packed floating-point operations. For example, register file 244 may include registers denoted MM0 through MMn which conform to the 3DNow!(trademark) and MMX(copyright) instruction set architectures. In one embodiment of microprocessor 110, there are eight MM registers, i.e. MM0 through MM7, each having a 64 bit storage capacity. Two 32-bit floating point operands may be loaded into each MM register in a packed format. For example, suppose register MM0 has been loaded with floating-point operands A and B, and register MM1 has been loaded with floating-point operands C and D. In shorthand notation, this situation may be represented by the expressions MM0=[A:B] and MM1=[C:D], where the first argument in a bracketed pair represents the high-order 32 bits of a quadword register, and the second argument represents the low-order 32 bits of the quadword register. The 3DNow!(trademark) instructions invoke parallel floating-point operations on the contents of the MM registers. For example, the 3DNow!(trademark) multiply instruction given by the assembly language construct
xe2x80x9cpfmul MM0,MM1xe2x80x9d
invokes a parallel floating-point multiply on corresponding components of MM0 and MM1. The two floating-point resultant values of the parallel multiply are stored in register MM0. Thus, after the instruction has completed execution, register MM0 may be represented by the expression MM0=[A*C:B*D]. As used herein, the assembly language construct
xe2x80x9cpfxxx MMdest, MMsrcxe2x80x9d
implies that a 3DNow!(trademark) operation corresponding to the mnemonic pfxxx uses registers MMdest and MMsrc as source operands, and register MMdest as a destination operand.
The assembly language construct
xe2x80x9cpfadd MM0,MM1xe2x80x9d
invokes a parallel floating-point addition on corresponding components of registers MM0 and MM1. Thus, after this instructions has completed execution, register MM0 may be represented by the expression MM0=[A+C:B+D].
It is noted that alternate embodiments of microprocessor 110 are contemplated where the storage capacity of an MM register allows for more than two floating-point operands. For example, an embodiment of microprocessor 110 is contemplated where the MM registers are configured to store four 32-bit floating-point operands. In this case, the MM registers may have a size of 128-bits.
Multimedia applications demand increasing amounts of storage and transmission bandwidth. Thus, multimedia systems use various types of audio/visual compression algorithms to reduce the amount of necessary storage and transfer bandwidth. In general, different video compression methods exist for still graphic images and for full-motion video. Intraframe compression methods are used to compress data within a still image or single frame using spatial redundancies within the frame. Interframe compression methods are used to compress multiple frames, i.e., motion video, using the temporal redundancy between the frames. Interframe compression methods are used exclusively for motion video, either alone or in conjunction with intrafrane compression methods.
Intraframe or still image compression techniques generally use frequency domain techniques, such as the discrete cosine transform (DCT). The frequency domain characteristics of a picture frame generally allow for easy removal of spatial redundancy and efficient encoding of the frame. One video data compression standard for still graphic images is JPEG (Joint Photographic Experts Group) compression. JPEG compression is actually a group of related standards that use the discrete cosine transform (DCT) to provide either lossless (no image quality degradation) or lossy (imperceptible to severe degradation) compression. Although JPEG compression was originally designed for the compression of still images rather than video, JPEG compression is used in some motion video applications.
In contrast to compression algorithms for still images, most video compression algorithms are designed to compress full motion video. As mentioned above, video compression algorithms for motion video use a concept referred to as interframe compression to remove temporal redundancies between frames. Interframe compression involves storing only the differences between successive frames in the data file. Interframe compression stores the entire image of a key frame or reference frame, generally in a moderately compressed format. Successive frames are compared with the key frame, and only the differences between the key frame and the successive frames are stored. Periodically, such as when new scenes are displayed, new key frames are stored, and subsequent comparisons begin from this new reference point. The difference frames are further compressed by such techniques as the DCT. Examples of video compression which use an interframe compression technique are MPEG (Moving Pictures Experts Group), DVI and Indeo, among others.
MPEG compression is based on two types of redundancies in video sequences, these being spatial, which is the redundancy in an individual frame, and temporal, which is the redundancy between consecutive frames. Spatial compression is achieved by considering the frequency characteristics of a picture frame. Each frame is divided into non-overlapping blocks, and each block is transformed via the discrete cosine transform (DCT). After the transformed blocks are converted to the xe2x80x9cDCT domainxe2x80x9d, each entry in the transformed block is quantized with respect to a set of quantization tables. The quantization step for each entry can vary, taking into account the sensitivity of the human visual system (HVS) to the frequency. Since the HVS is more sensitive to low frequencies, most of the high frequency entries are quantized to zero. In this step where the entries are quantized, information is lost and errors are introduced to the reconstructed image. Run length encoding is used to transmit the quantized values. To further enhance compression, the blocks are scanned in a zig-zag ordering that scans the lower frequency entries first, and the non-zero quantized values, along with the zero run lengths, are entropy encoded.
As discussed above, temporal compression makes use of the fact that most of the objects remain the same between consecutive picture frames, and the difference between objects or blocks in successive frames is their position in the frame as a result of motion (either due to object motion, camera motion or both). This relative encoding is achieved by the process of motion estimation. The difference image as a result of motion compensation is further compressed by means of the DCT, quantization and RLE entropy coding.
When an MPEG decoder receives an encoded stream, the MPEG decoder reverses the above operations. Thus the MPEG decoder performs inverse scanning to remove the zig zag ordering, inverse quantization to de-quantize the data, and the inverse DCT to convert the data from the frequency domain back to the pixel domain. The MPEG decoder also performs motion compensation using the transmitted motion vectors to re-create the temporally compressed frames.
Computation of the discrete cosine transform (DCT) as well as computation of the inverse discrete cosine transform (IDCT) in multimedia systems generally require a large amount of processing. For example, hundreds of multiplication (or division) operations as well as hundreds of addition (or subtraction) operations may be required to perform the DCT or IDCT upon a single 8xc3x978 array. Such computational requirements can be extremely time-consuming and resource intensive.
A new system and method are desired for efficiently computing the forward and/or inverse discrete cosine transform. It is particularly desirable to provide a system for computing the forward and/or inverse discrete cosine transform which reduces computational requirements in a general purpose computer system.
The problems outlined above are in large part solved by a system and method of a two-dimensional forward and/or inverse discrete cosine transform in accordance with the present invention. In one embodiment, the method comprises: (1) receiving multiple data blocks; (2) grouping together one respective element from each of the multiple data blocks to provide full data vectors for single-instruction-multiple-data (SIMD) floating point instructions; and (3) operating on the full data vectors with SIMD instructions to carry out the two dimensional transform on the multiple data blocks. Preferably the two dimensional transform is carried out by performing a linear transform on each row of the grouped elements, and then performing a linear transform on each column of the grouped elements. The method may further include isolating and arranging the two dimensional transform coefficients to form transform coefficient blocks that correspond to the originally received multiple data blocks. The multiple data blocks may consist of exactly two data blocks. The method may be implemented in the form of software and conveyed on a digital information storage medium or information transmission medium. The dual forward or inverse discrete cosine transform methodology may be employed within a general purpose computer or within a computation unit of a multimedia encoder or decoder system, implemented either in hardware or software. A multimedia encoder or decoder employing the fast, forward or inverse discrete cosine transform methodology in accordance with the present invention may advantageously achieve high performance.